is used to train the feature encoder with a constraint to the output latent variables from a known random probability distribution \(p(\mathbf {z}),\)  from which we can easily sample. The constant \(\beta \)  is a hyper-parameter that regularizes the latent vector for better disentanglement representation of the data [1]}, [2]}, [3]}.
